First, this setting introduces a domain shift between the images used to pre-train the model and those targeted by the fine-tuning stage. Imagenet images may be sufficiently representative of natural images (despite the collecting bias).
To date, most researchers consider that the benefit of having a large amount of images vastly compensates the domain discrepancy on benchmarks involving natural images, such as the fine-grained iNaturalist datasets [1]}, [2]} or even out-of-domain distributions such as sketches, painting or clipart.
